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Google Summer of Code 2019 Ideas
Ideas for Google Summer of Code 2019.
Feel free to reach us by joining #sciruby
on chat.freenode.net
or via our mailing list.
IMPORTANT NOTICE: SciRuby encourages diversity. Scientific progress in general benefits from diversity and software development for science is no exception. We are really happy that the number of people from Asia, Africa and South America applying for GSoC projects is increasing. Our org admin this year is from India, our previous org admin was from Brazil. We have had students from Japan, India, Sri Lanka, Russia, etc. We have women software developers in our programme. We are happy to hear from you all!
We strongly recommend that you pick one of the ideas listed below. We value contributions in advance of GSoC, even if they're just little ones. Go pick out something in one of our trackers and work on it, talk to folks on the listserv, and get an idea for what features are needed.
You don't need to know a lot about Ruby to work on a project: depending on how much you already know, it'll be pretty easy to learn enough to be able to contribute. However, you may need some familiarity with scientific computation. If you don't have any, take a look at "Numerical Recipes in C", which you'll probably find in your university's library.
In any case, if you feel your skills aren't enough for some project, please ask us on our IRC channel (see contact section above) or our Google Group (see sciruby.com to sign up) and we can help you.
See also:
Most of the main SciRuby’s landing page on Github holds the stable version of SciRuby gems but developers and contributors should work on the very latest (bleeding edge) repositories in order to make sure that changes can be committed without conflict arising.
Try reading Finding The SciRuby Development Repositories on Github if you would like a brief introduction on finding the latest development gems to work on from Github. Also go through the coding guidelines before sending your first patch.
Here's a great tutorial: http://www.thinkful.com/learn/github-pull-request-tutorial/
Have a look and feel free to ask if you have any questions.
Guidelines for mentors to submit projects:
- Specify the name of your project as a heading.
- Write a paragraph or two with further details.
- Write a small 'Skills' section detailing the skills that the student must possess to complete the project.
- Write down your own GitHub handle and contact details in a 'Mentor Details' section over which the student can contact you.
- If anyone else wants to co-mentor a project, please specify your details along with the mentor's details.
NMatrix is SciRuby's numerical matrix core, implementing dense matrices as well as two types of sparse (linked-list-based and Yale/CSR). NMatrix is a fairly well-established project which has received Summer-of-Code-like grants from both Brighter Planet and the Ruby Association (in other words, from Matz, who created Ruby). Those who contribute to NMatrix will likely eventually become authors of a jointly-published peer-reviewed science article on the library. Additionally, NMatrix is a good place to gain practical C and C++ experience, while also working to improve Ruby.
NMatrix currently relies on ATLAS/CBLAS/CLAPACK and standard LAPACK for several of its linear algebra operations. In some cases, native versions of the functions are implemented, so that the libraries are not required. There are quite a number of areas for growth in terms of the capabilities of NMatrix here.
- NMatrix reloaded is a reimplementation of NMatrix. It is faster than existing NMatrix (See link).
- The student needs to work on implementing multiple dtypes and stypes in Ruby. Implemeting Yale notation is a priority.
- Implement indexers for NMatrix.
- Mentors: Pjotr Prins(@pjotrp)(?) Prasun Anand(@prasunanand) , , Co-mentor - Shekhar (@Shekharrajak)
- Recommended skills: Some C/C++ would be beneficial, as you'll need to be working under the hood on NMatrix.
Learn basics of daru-view, from sciruby/blog or daru-view/wiki.
Daru (Data Analysis in RUby) is a library for analysis, manipulation and visualization of data. daru-view is for easy and interactive plotting in web application & IRuby notebook. It can work in frameworks like Rails, Sinatra, Nanoc and hopefully in others too.
It is a plugin gem to Data Analysis in RUby(Daru) for visualisation of data
Currently daru-view have dependencies with lazy_high_charts and googlevisualr, where SciRuby don't have any control. We have solved problems like (mainly):
- daru dataframe or vector compatible plotting gem.
- a gem that can work smoothly in any Ruby web application framework, IRuby notebook as well as terminal.
So now it is the time to be independent
Because -
-
we don't have much control over these gems and also we will be keep adding new features directly from HighCharts and Google Charts official sites.
-
we have extended (overload and override) most of the methods from lazy_high_charts and googlevisualr, to make it compatible for IRuby notebook and all ruby frameworks or to add new chart features already presents in HighCharts and Google Charts.
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daru-view should be able to handle future chart types as well without (or very less) modifying codebase.
You can find more details about in this wiki page - 'Making daru-view independent'.Along with this we also want to consider new ideas written in Idea wiki page
- daru-view/wiki/ideas
- Discussion in sciruby mailing thread
- Shekhar's blog post: GSoC 2017
- GSoC 2018 Progress Report
- GSoC 2018 discussion
- Skills: Basic knowledge of Ruby, Design pattern and Design Principles, Javascript and Ruby web application frameworks.
- Mentors: Shekhar (@Shekharrajak), Sameer (@v0dro)
- Difficulty: Moderate.
Visualization is one of the single most important things for any non-trivial scientific stack, and Ruby is seriously lacking any serious support for a comprehensive plotting solution. Rubyplot aims to fill that gap.
Rubyplot aims to be the best visualization framework in Ruby for plotting anything, anywhere.
It started off as a GSOC 2018 project and has since undergone a complete rewrite. Visualization is a very important focus area for the Ruby community at this point of time, and successful student proposals should expect their work to be accepted into major Ruby conferences worldwide (which usually includes free travel ;) ).
Working on this project will mean working closely with both the international and Japanese Ruby communities and identifying use cases, usage patterns and how best to reflect them in your work. Expect lots of collaboration and networking from this project.
Read up the README and CONTRIBUTING guides in the rubyplot repo to get a brief overview of the current state of the library. Some of the projects listed might not be 3 full months of work so you should think about clubbing one or more projects together.
Following are GSOC 2019 projects for rubyplot:
In the current state, Line
and Scatter
plots exist as two different kinds of plots. However, the crux of plotting both of these is exactly the same, the only difference being that in one kind of plot we have straight lines connected to co-ordinates while in the other the co-ordinates are simply 'decorated'. Thus can be many combinations of these plots, which can be combined together under a single 'plot' interface. This task will involve the following:
- Write a 'plot' function for
Rubyplot::Artist::Axes
that is similar in function to matplotlib'splot
function. - Support all the properties of the
plot
function as per matplotlib using a Ruby-like interface.
This project will greatly enhance the plotting interface of Rubyplot and pave the way for much greater expansion.
- Difficulty: Moderate
- Skills: Ruby, familiarity with visualization.
- Mentors: @v0dro (Sameer)
Currently the support for various types of plots is very limited. This project will involve two things:
- Adding support for multiple Axes in the same Figure.
- Adding support for various kinds of plots as listed here.
The above list is not exhaustive. The student should feel free to propose their own plots.
- Difficulty: Moderate
- Skills: Ruby, geometry, familiarity with visualization.
- Mentors: @v0dro (Sameer Deshmukh), Co-mentor - (Shekhar @Shekharrajak)